Comparative Study of Various Machine Learning Algorithms for Prediction of Insomnia

Ahuja, Ravinder; Vishal, Vivek; Chandna, Manika; Virmani, Shivani; Banga, Alisha

doi:10.4018/978-1-6684-6291-1.ch041

Cited by 3 publications

(3 citation statements)

References 37 publications

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“…Prior studies have accurately predicted the presence of sleep disorders using machine-learning methods from a variety of datasets using numerous machine-learning methods [36][37][38]. Short-term insomnia detection was conducted using a single-channel sleep Electrooculography.…”

Section: Discussionmentioning

confidence: 99%

“…Short-term insomnia detection was conducted using a single-channel sleep Electrooculography. Furthermore, natural language processing on 18,901 tweets was conducted to find correlations between words related to insomnia and negative health information [38][39][40]. Furthermore, a comparative study of 15 machine learning algorithms identified 14 main factors for the prediction of insomnia, identifying that vision problems, mobility problems, and sleep disorders were significantly related to insomnia [38,39].…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, natural language processing on 18,901 tweets was conducted to find correlations between words related to insomnia and negative health information [38][39][40]. Furthermore, a comparative study of 15 machine learning algorithms identified 14 main factors for the prediction of insomnia, identifying that vision problems, mobility problems, and sleep disorders were significantly related to insomnia [38,39]. These studies highlight the utility of machine learning models in identifying patients at risk for sleep disorders.…”

Section: Discussionmentioning

confidence: 99%

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Use of machine learning to identify risk factors for insomnia

Huang

2023

PLoS ONE

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Importance Sleep is critical to a person’s physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders. Objective The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology. Design, setting, and participants A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Methods A physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia. Results Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P<0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%). Conclusion Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Use of machine learning to identify risk factors for insomnia

Huang

2023

PLoS ONE

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Modeling acute care utilization: practical implications for insomnia patients

Chekani

Zhu

Khandker

et al. 2023

Sci Rep

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Machine learning models can help improve health care services. However, they need to be practical to gain wide-adoption. In this study, we investigate the practical utility of different data modalities and cohort segmentation strategies when designing models for emergency department (ED) and inpatient hospital (IH) visits. The data modalities include socio-demographics, diagnosis and medications. Segmentation compares a cohort of insomnia patients to a cohort of general non-insomnia patients under varying age and disease severity criteria. Transfer testing between the two cohorts is introduced to demonstrate that an insomnia-specific model is not necessary when predicting future ED visits, but may have merit when predicting IH visits especially for patients with an insomnia diagnosis. The results also indicate that using both diagnosis and medications as a source of data does not generally improve model performance and may increase its overhead. Based on these findings, the proposed evaluation methodologies are recommended to ascertain the utility of disease-specific models in addition to the traditional intra-cohort testing.

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Stress Detection in IT Professionals using Image Processing and Machine Learning

Asha Shinde,

Gayatri Adsure,

Tanvi Borge

et al. 2024

IJARSCT

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The growing needs of the IT enterprise often disclose experts to chronic stress, that could lead to burnout and reduced productiveness. This paper gives a system for real-time strain detection among IT specialists using the Logistic Regression technique. The proposed gadget leverages physiological statistics from wearable sensors, including heart charge, to screen pressure ranges non-invasively. Logistic Regression is used because the number one algorithm for classifying stress levels primarily based on the amassed information. Existing techniques, including surveys and facial popularity, are frequently invasive and unreliable for real-time packages. The proposed technique addresses these obstacles by means of providing a more scalable and green solution that may be seamlessly integrated into place of work environments. By allowing early detection and intervention, this machine no longer most effectively helps in coping with stress however additionally promotes mental well-being and complements productiveness. The paper indicates that the Logistic Regression-primarily based version may be tailored to be used in different high-strain industries, inclusive of healthcare and education, presenting broader programs for strain control.

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Comparative Study of Various Machine Learning Algorithms for Prediction of Insomnia

Cited by 3 publications

References 37 publications

Use of machine learning to identify risk factors for insomnia

Use of machine learning to identify risk factors for insomnia

Modeling acute care utilization: practical implications for insomnia patients

Stress Detection in IT Professionals using Image Processing and Machine Learning

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